update and fix

This commit is contained in:
padreug 2025-06-22 17:38:24 +02:00
parent c3adc37d84
commit 234daebef7
3 changed files with 473 additions and 115 deletions

324
crud.py
View file

@ -197,114 +197,228 @@ async def get_client_transactions(
async def get_client_analytics(user_id: str, time_range: str = "30d") -> Optional[ClientAnalytics]:
"""Get client performance analytics"""
# Get client ID
client = await db.fetchone(
"SELECT id FROM satmachineadmin.dca_clients WHERE user_id = :user_id",
{"user_id": user_id}
)
try:
from datetime import datetime
# Get client ID
client = await db.fetchone(
"SELECT id FROM satmachineadmin.dca_clients WHERE user_id = :user_id",
{"user_id": user_id}
)
if not client:
print(f"No client found for user_id: {user_id}")
return None
print(f"Found client {client['id']} for user {user_id}, loading analytics for time_range: {time_range}")
if not client:
return None
# Calculate date range
if time_range == "7d":
start_date = datetime.now() - timedelta(days=7)
elif time_range == "30d":
start_date = datetime.now() - timedelta(days=30)
elif time_range == "90d":
start_date = datetime.now() - timedelta(days=90)
elif time_range == "1y":
start_date = datetime.now() - timedelta(days=365)
else: # "all"
start_date = datetime(2020, 1, 1) # Arbitrary early date
# Get cost basis history (running average)
cost_basis_data = await db.fetchall(
"""
SELECT
created_at,
amount_sats,
amount_fiat,
exchange_rate,
SUM(amount_sats) OVER (ORDER BY created_at) as cumulative_sats,
SUM(amount_fiat) OVER (ORDER BY created_at) as cumulative_fiat
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id
AND status = 'confirmed'
AND created_at >= :start_date
ORDER BY created_at
""",
{"client_id": client["id"], "start_date": start_date}
)
# Build cost basis history
cost_basis_history = []
for record in cost_basis_data:
avg_cost_basis = record["cumulative_sats"] / record["cumulative_fiat"] if record["cumulative_fiat"] > 0 else 0
cost_basis_history.append({
"date": record["created_at"].isoformat(),
"average_cost_basis": avg_cost_basis,
"cumulative_sats": record["cumulative_sats"],
"cumulative_fiat": record["cumulative_fiat"]
})
# Get accumulation timeline (daily/weekly aggregation)
accumulation_data = await db.fetchall(
"""
SELECT
DATE(created_at) as date,
SUM(amount_sats) as daily_sats,
SUM(amount_fiat) as daily_fiat,
COUNT(*) as daily_transactions
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id
AND status = 'confirmed'
AND created_at >= :start_date
GROUP BY DATE(created_at)
ORDER BY date
""",
{"client_id": client["id"], "start_date": start_date}
)
accumulation_timeline = [
{
"date": record["date"],
"sats": record["daily_sats"],
"fiat": record["daily_fiat"],
"transactions": record["daily_transactions"]
# Calculate date range
if time_range == "7d":
start_date = datetime.now() - timedelta(days=7)
elif time_range == "30d":
start_date = datetime.now() - timedelta(days=30)
elif time_range == "90d":
start_date = datetime.now() - timedelta(days=90)
elif time_range == "1y":
start_date = datetime.now() - timedelta(days=365)
else: # "all"
start_date = datetime(2020, 1, 1) # Arbitrary early date
# Get cost basis history (running average)
cost_basis_data = await db.fetchall(
"""
SELECT
COALESCE(transaction_time, created_at) as transaction_date,
amount_sats,
amount_fiat,
exchange_rate,
SUM(amount_sats) OVER (ORDER BY COALESCE(transaction_time, created_at)) as cumulative_sats,
SUM(amount_fiat) OVER (ORDER BY COALESCE(transaction_time, created_at)) as cumulative_fiat
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id
AND status = 'confirmed'
AND COALESCE(transaction_time, created_at) IS NOT NULL
AND COALESCE(transaction_time, created_at) >= :start_date
ORDER BY COALESCE(transaction_time, created_at)
""",
{"client_id": client["id"], "start_date": start_date}
)
# Build cost basis history
cost_basis_history = []
for record in cost_basis_data:
avg_cost_basis = record["cumulative_sats"] / record["cumulative_fiat"] if record["cumulative_fiat"] > 0 else 0
# Use transaction_date (which is COALESCE(transaction_time, created_at))
date_to_use = record["transaction_date"]
if date_to_use is None:
print(f"Warning: Null date in cost basis data, skipping record")
continue
elif hasattr(date_to_use, 'isoformat'):
# This is a datetime object
date_str = date_to_use.isoformat()
elif hasattr(date_to_use, 'strftime'):
# This is a date object
date_str = date_to_use.strftime('%Y-%m-%d')
elif isinstance(date_to_use, (int, float)):
# This might be a Unix timestamp - check if it's in a reasonable range
timestamp = float(date_to_use)
# Check if this looks like a timestamp (between 1970 and 2100)
if 0 < timestamp < 4102444800: # Jan 1, 2100
# Could be seconds or milliseconds
if timestamp > 1000000000000: # Likely milliseconds
timestamp = timestamp / 1000
date_str = datetime.fromtimestamp(timestamp).isoformat()
else:
# Not a timestamp, treat as string
date_str = str(date_to_use)
print(f"Warning: Numeric date value out of timestamp range: {date_to_use}")
elif isinstance(date_to_use, str) and date_to_use.isdigit():
# This is a numeric string - might be a timestamp
timestamp = float(date_to_use)
# Check if this looks like a timestamp
if 0 < timestamp < 4102444800: # Jan 1, 2100
# Could be seconds or milliseconds
if timestamp > 1000000000000: # Likely milliseconds
timestamp = timestamp / 1000
date_str = datetime.fromtimestamp(timestamp).isoformat()
else:
# Not a timestamp, treat as string
date_str = str(date_to_use)
print(f"Warning: Numeric date string out of timestamp range: {date_to_use}")
else:
# Convert string representation to proper format
date_str = str(date_to_use)
print(f"Warning: Unexpected date format: {date_to_use} (type: {type(date_to_use)})")
cost_basis_history.append({
"date": date_str,
"average_cost_basis": avg_cost_basis,
"cumulative_sats": record["cumulative_sats"],
"cumulative_fiat": record["cumulative_fiat"]
})
# Get accumulation timeline (daily/weekly aggregation)
accumulation_data = await db.fetchall(
"""
SELECT
DATE(COALESCE(transaction_time, created_at)) as date,
SUM(amount_sats) as daily_sats,
SUM(amount_fiat) as daily_fiat,
COUNT(*) as daily_transactions
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id
AND status = 'confirmed'
AND COALESCE(transaction_time, created_at) IS NOT NULL
AND COALESCE(transaction_time, created_at) >= :start_date
GROUP BY DATE(COALESCE(transaction_time, created_at))
ORDER BY date
""",
{"client_id": client["id"], "start_date": start_date}
)
accumulation_timeline = []
for record in accumulation_data:
# Handle date conversion safely
date_value = record["date"]
if date_value is None:
print(f"Warning: Null date in accumulation data, skipping record")
continue
elif hasattr(date_value, 'isoformat'):
# This is a datetime object
date_str = date_value.isoformat()
elif hasattr(date_value, 'strftime'):
# This is a date object (from DATE() function)
date_str = date_value.strftime('%Y-%m-%d')
elif isinstance(date_value, (int, float)):
# This might be a Unix timestamp - check if it's in a reasonable range
timestamp = float(date_value)
# Check if this looks like a timestamp (between 1970 and 2100)
if 0 < timestamp < 4102444800: # Jan 1, 2100
# Could be seconds or milliseconds
if timestamp > 1000000000000: # Likely milliseconds
timestamp = timestamp / 1000
date_str = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d')
else:
# Not a timestamp, treat as string
date_str = str(date_value)
print(f"Warning: Numeric accumulation date out of timestamp range: {date_value}")
elif isinstance(date_value, str) and date_value.isdigit():
# This is a numeric string - might be a timestamp
timestamp = float(date_value)
# Check if this looks like a timestamp
if 0 < timestamp < 4102444800: # Jan 1, 2100
# Could be seconds or milliseconds
if timestamp > 1000000000000: # Likely milliseconds
timestamp = timestamp / 1000
date_str = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d')
else:
# Not a timestamp, treat as string
date_str = str(date_value)
print(f"Warning: Numeric accumulation date string out of timestamp range: {date_value}")
else:
# Convert string representation to proper format
date_str = str(date_value)
print(f"Warning: Unexpected accumulation date format: {date_value} (type: {type(date_value)})")
accumulation_timeline.append({
"date": date_str,
"sats": record["daily_sats"],
"fiat": record["daily_fiat"],
"transactions": record["daily_transactions"]
})
# Get transaction frequency metrics
frequency_stats = await db.fetchone(
"""
SELECT
COUNT(*) as total_transactions,
AVG(amount_sats) as avg_sats_per_tx,
AVG(amount_fiat) as avg_fiat_per_tx,
MIN(COALESCE(transaction_time, created_at)) as first_tx,
MAX(COALESCE(transaction_time, created_at)) as last_tx
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id AND status = 'confirmed'
""",
{"client_id": client["id"]}
)
# Build transaction frequency with safe date handling
transaction_frequency = {
"total_transactions": frequency_stats["total_transactions"] if frequency_stats else 0,
"avg_sats_per_transaction": frequency_stats["avg_sats_per_tx"] if frequency_stats else 0,
"avg_fiat_per_transaction": frequency_stats["avg_fiat_per_tx"] if frequency_stats else 0,
"first_transaction": None,
"last_transaction": None
}
for record in accumulation_data
]
# Handle first_tx date safely
if frequency_stats and frequency_stats["first_tx"]:
first_tx = frequency_stats["first_tx"]
if hasattr(first_tx, 'isoformat'):
transaction_frequency["first_transaction"] = first_tx.isoformat()
else:
transaction_frequency["first_transaction"] = str(first_tx)
# Handle last_tx date safely
if frequency_stats and frequency_stats["last_tx"]:
last_tx = frequency_stats["last_tx"]
if hasattr(last_tx, 'isoformat'):
transaction_frequency["last_transaction"] = last_tx.isoformat()
else:
transaction_frequency["last_transaction"] = str(last_tx)
# Get transaction frequency metrics
frequency_stats = await db.fetchone(
"""
SELECT
COUNT(*) as total_transactions,
AVG(amount_sats) as avg_sats_per_tx,
AVG(amount_fiat) as avg_fiat_per_tx,
MIN(created_at) as first_tx,
MAX(created_at) as last_tx
FROM satmachineadmin.dca_payments
WHERE client_id = :client_id AND status = 'confirmed'
""",
{"client_id": client["id"]}
)
transaction_frequency = {
"total_transactions": frequency_stats["total_transactions"] if frequency_stats else 0,
"avg_sats_per_transaction": frequency_stats["avg_sats_per_tx"] if frequency_stats else 0,
"avg_fiat_per_transaction": frequency_stats["avg_fiat_per_tx"] if frequency_stats else 0,
"first_transaction": frequency_stats["first_tx"].isoformat() if frequency_stats and frequency_stats["first_tx"] else None,
"last_transaction": frequency_stats["last_tx"].isoformat() if frequency_stats and frequency_stats["last_tx"] else None
}
return ClientAnalytics(
user_id=user_id,
cost_basis_history=cost_basis_history,
accumulation_timeline=accumulation_timeline,
transaction_frequency=transaction_frequency
)
return ClientAnalytics(
user_id=user_id,
cost_basis_history=cost_basis_history,
accumulation_timeline=accumulation_timeline,
transaction_frequency=transaction_frequency
)
except Exception as e:
print(f"Error in get_client_analytics for user {user_id}: {str(e)}")
import traceback
traceback.print_exc()
return None
async def get_client_by_user_id(user_id: str):